AI @ Monsignor William Barry Memorial LIbrary: AI Glossary of Terms

Generative AI refers to a category of artificial intelligence techniques and models that are designed to generate content autonomously, often in the form of text, images, music, or other forms of creative output. These models use patterns learned from large datasets to create new content that is similar in style, structure, and context to the examples they were trained on.

One of the key advancements in generative AI is the development of models like Generative Pre-trained Transformers (GPT), which are trained on massive amounts of text data to understand and replicate human language patterns. These models can generate coherent and contextually relevant text based on a given prompt.

Generative AI can be used for various purposes, including:

1. Text Generation: Models like GPT-3 can generate human-like text based on prompts, write articles, answer questions, and even create poetry.

2. Image Generation: Models like DALL-E can generate images from textual descriptions, allowing users to "describe" an image they want, and the model creates it.

3. Music Composition: Generative AI models can create music compositions based on certain styles or artists, producing new melodies and harmonies.

4. Video Generation: Some generative models can generate videos by extrapolating from existing video footage or creating entirely new sequences.

5. Style Transfer: Generative models can transfer the style of one image onto the content of another, creating visually appealing combinations.

6. Data Augmentation: Generative models can be used to create additional training data for machine learning algorithms, helping to improve their performance.

"Generated by OpenAI's GPT-3 model (ChatGPT). OpenAI. (2023, August 9). Message generated using GPT-3 model. Retrieved from [https://openai.com]"

• Algorithm: a process or set of rules that tell computers and software applications how to learn to perform tasks, make decisions, and analyze data independently
• Artificial Intelligence (AI): the simulation of human intelligence in machines that are programmed to think and learn
• Bias and Variance: concepts in machine learning that describe the errors from the model. Bias is error from overly simplistic assumptions, while variance is error from sensitivity to small fluctuations in the training set.
• Big Data: large and complex data sets that traditional data-processing software cannot deal with adequately
• Chatbot: a software application or web interface designed to mimic human conversation through text or voice interactions
• ChatGPT: an AI chatbot that uses natural language processing (NLP) and machine learning algorithms to generate a response; created by OpenAI and trained on a large amount of text and code data to produce relevant responses
• Deep Learning (DL): a type of machine learning that uses neural networks with many layers (deep networks) to analyze various factors of data
• Generative AI: a type of AI that uses deep learning models to create new content based on patters in existing data; can be trained to learn a variety of subjects, including human and programming languages
• Large Language Model (LLM): an advanced AI system capable of understanding and generating human-like text by leveraging patterns learned from extensive datasets
• Machine Learning (ML): a subset of AI that allows computers to learn from data and make decisions without being explicitly programmed to do so
• Neural Network: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates
• Prompt Engineering: the process of designing inputs or prompts for generative AI tools to produce desired outputs